How to resolve job submitting error in FSL dual regression command line - command

I perform multi session temporal concatenation FSL tool on my 4D fMRI data, and now I am running dual regression on those preprocessed, standard-space data, which are preprocessed fMRI data and registered to standard space (atlas), but I am receiving a job submitting error and I don't know what is wrong with it. here is the command I am using:
[dual_regression Participants/GroupICA.gica/groupmelodic.ica/melodic_IC.nii.gz 1 Designs/design.mat Designs/design.con 5000 gICA.dr `cat inputslist.txt`][1]
I attach the error I am receiving as well.enter image description here
I appreciate your help if you know how I can solve this issue.
Thank you and looking forward to hearing from you
Laila

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Can't read serial response using pyserial

Trying to use pyserial to talk to a Korad KD3005P power supply. (Using Python 3.6)
import serial
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ser.write(b'VSET1:2')
ser.write(b'VOUT1?')
response = ser.readline()
print(response)
VSET1:2 sets the voltage to 2 volts
VOUT1? tells the device to return the current voltage.
But all I get from the VOUT1? part seems to be:
b''
Stuff I've checked or tried without success:
The device works - I can talk to the device and get responses successfully with these commands through a serial terminal like CuteCom
Pyserial is kinda working - if I run the above program, the VSET1:2 does change the voltage.
I've tried different timeouts
I've tried ser.read() instead with various bit lengths
I've tried putting the read commands in a while loop
Any suggestions?
Oh.
It's a timing thing of some kind. Removing the VSET command or putting a time.sleep() command in between them and voila.. I get a response.
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Filtering an audio signal and then reading the meter without sending it to master

I'm trying to filter a signal and then analyse the values of the filtered signal using Tone.js / Web-Audio API.
I'm expecting to get values of the filtered signal, but I only get -Infinity, meaning that my connections between the nodes are wrong. I've made a small fiddle demonstrating this, however in my use-case I do not want to send this node to the destination of the context - I only want to analyse it, not hear it.
osc.connect(filter)
filter.connect(gainNode)
gainNode.connect(meter)
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I guess you tested the code in Chrome because there is a problem with Chrome which causes it to not process anything until it is connected to the destination. When using Tone.js that means you need to call .toMaster() at the end of your chain. I updated you fiddle to make it work: https://jsfiddle.net/8f7abzoL/.
In Firefox calling .toMaster() is not necessary therefore the following works in Firefox as well: https://jsfiddle.net/yrjgfdtz/.
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How can I prevent this error from happening?
You prevent the error from happening in one of two ways:
fix your Promela model - you have a bug in your Promela that needs to be removed
you've found an error in the system that you are modeling. Congratulations. You now report the error to the person in charge of the system that you are modeling.
In practice, when you have a trail indicating an error, you next perform
spin -p -t <name of the model>.pml
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Any reason why these instance could be misclassified?

I started off with two files training & testing.
Then using libsvm I scaled both those files to training.scale and testing.scale
Then using grid.py (part of libsvm) I ran training.scale and and recieved some cross validation values:
C = 512
gamme = 0.03125
validation 5 = 66.8421
Then running svm-train using the variable found from grid.py and training.scale I got a new fine called training.scale.model
I then ran svm-predict and I new file called testing.predict and got a validation % of 60.8333%
Finally comparing testing and testing.predict found that there were 47/120 misclassifications
[https://drive.google.com/folderview?id=0BxzgP5V6RPQHekRjZXdFYW9GX0U&usp=sharing][1]
[1]: link to code
The real question is there any reason why these misclassification occur?
PS. I apologise for the bad format of this question, been up for too long
I am guessing you are new to machine learning. The results you've got are perfectly right.
Reason why these mis-classifications occur? The features you've used don't separate your classes well. A 66% cross-validation score should have given you the hint. Even by plain hit or miss method you'll get 50% accuracy, and the feature-set you used could only improve this by another 16%. Try exploring new features.
I'm assuming your data set is clean.

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if (simGetScriptExecutionCount()==0) then
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local pluginNotFound=true
while moduleName do
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if (moduleName=='Ros') then
pluginNotFound=false
end
index=index+1
end
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if (topicName == nil) then
simDisplayDialog('Error','Nil',sim_dlgstyle_ok,false,nil,{0.8,0,0,0,0,0},{0.5,0,0,1,1,1})
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Any idea is welcome! Thanks in advance!
PS: Also, if anyone with 1500 reputation points could create the v-rep tag, that would be great!
Ok, the people at V-REP helped me solve it. So basically I should used the argument as -1 instead of 0.
So basically, a should have done this
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instead of
topicName=simExtROS_enablePublisher('p3dx',1,simros_strmcmd_get_object_pose,lHandle,0,'')